Note: The retrieved evidence partially covers this topic. I can support the core concepts below from the evidence, but cannot provide a complete mechanistic walkthrough beyond what the sources directly state.
Core Mechanics (Evidence-Supported)
Reinforcement learning agents must discover a solution on their own, using learning, rather than relying on preprogrammed behaviors. Buşoniu et al. (2008)1identify two focal goals in the field: stability of the agents' learning dynamics and adaptation to the changing behavior of other agents1
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1
A Comprehensive Survey of Multiagent Reinforcement LearningLucian Buşoniu, Robert Babuška et al.2008IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
Rewards can be sparse and depend on complex sequences of actions, making learning difficult in many real-world settings. Corazza et al. (2025)2specifically address the challenge of noisy reward functions, noting that existing algorithms "assume an overly idealized setting where rewards have to be free of noise"2. Their approach guarantees convergence to an optimal policy in the limit2
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Reinforcement Learning with Stochastic Reward MachinesJan Corazza, Ivan Gavran et al.2025arXiv
Policy gradient methods can be used to reward sequences displaying desired properties. Li et al. (2016) demonstrate this in dialogue, using policy gradient to reward sequences for informativity, coherence, and ease of answering . That work also highlights a key limitation of naive reward design: standard neural models "tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes" , motivating modeling of future reward over long horizons .
The retrieved papers do not provide definitions of core primitives (Markov Decision Processes, value functions, Q-learning, etc.), so I cannot support those from this evidence set. The answer above is bounded by what the three retrieved sources directly state.
